Papers by Ahmed Abbasi
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts (2022.acl-long)
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| Challenge: | Existing methods to mitigate human-like biases in pretrained language models are based on external corpora and require a distribution alignment loss to mitigate them. |
| Approach: | They propose an automatic method to mitigate biases in pretrained language models by searching for biased prompts such that cloze-style completions are the most different with respect to different demographic groups. |
| Outcome: | The proposed method reduces biases in pretrained language models, including gender and racial bias, and improves fairness of the models. |
Benchmarking Intersectional Biases in NLP (2022.naacl-main)
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| Challenge: | Recent work on fairness of machine learning models has focused on how to debias, but research on the fairness and performance of biased/debiased models on downstream prediction tasks has been limited. |
| Approach: | They assess intersectional bias - fairness across multiple demographic dimensions . they highlight possible causes and make recommendations for future NLP debiasing research. |
| Outcome: | The proposed approaches fare well in terms of fairness-accuracy trade-off, but are unable to effectively alleviate bias in downstream tasks. |
No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks (2025.naacl-long)
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| Challenge: | Understanding data complexity at the instance level has become increasingly important in Natural Language Processing (NLP) and machine learning (ML). |
| Approach: | They empirically examine the relationship between instance-level complexity scores and metric selection for classification tasks. |
| Outcome: | The results show that storing training loss provides similar complexity rankings to other methods, but not demographic fairness, even in downstream predictions. |
BARLE: Background-Aware Representation Learning for Background Shift Out-of-Distribution Detection (2022.findings-emnlp)
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| Challenge: | Existing methods for OOD detection focus on identifying semantic-shift OOD samples . background-shift detection is more practical but challenging . |
| Approach: | They propose a background-aware representation learning approach for background-shift OOD detection in NLP. |
| Outcome: | The proposed method improves background-shift OOD detection while maintaining ID classification accuracy. |
Textagon: Boosting Language Models with Theory-guided Parallel Representations (2025.acl-demo)
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| Challenge: | Pretrained language models do not account for the wide variety of available expert-generated language resources and lexicons that explicitly encode linguistic/domain knowledge. |
| Approach: | They propose a Python package for generating parallel representations for text based on predefined lexicons and selecting representations that provide the most information. |
| Outcome: | The proposed model can generate parallel representations of text based on predefined lexicons and select representations that provide the most information. |
Constructing a Psychometric Testbed for Fair Natural Language Processing (2021.emnlp-main)
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| Challenge: | Psychometric dimensions are important for understanding user behavior in various contexts including health, security, e-commerce, and finance. |
| Approach: | They propose to construct a corpus for psychometric natural language processing related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. |
| Outcome: | The proposed corpus includes 8,502 user-generated responses from 8,502-person survey datasets and includes self-reported demographic information, including race, sex, age, income, and education. |
Generalization or Memorization? Multi-Agent vs. Baseline LLMs and AutoML Models for Tabular Classification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for structured tabular data. |
| Approach: | They evaluate a representative modular Multi-Agent LLM framework against state-of-the-art AutoML systems and established baselines. |
| Outcome: | The proposed model outperforms AutoML on pre-cutoff and post-cut off datasets. |
Exploring the Relationship between In-Context Learning and Instruction Tuning (2024.findings-emnlp)
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| Challenge: | In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications, but they are significantly different. |
| Approach: | They examine how the hidden states of Large Language Models change in these two paradigms by examining how they differ in implementation. |
| Outcome: | The proposed model changes the hidden states of LLMs as if its accompanying demonstrations were used to instructionally tune the model. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
Dash-M5H: An Interactive Dashboard for Multi-Modal, Multi-Model Mental Health Assessment (2026.acl-demo)
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Raymond Alavo, Xinyuan Zhang, Gemza Ademaj, Junhui Cai, Hyeokhyen Kwon, Robert Cotes, Gari D. Clifford, Ahmed Abbasi
| Challenge: | Dash-M5H integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline that produces DSM-5-aligned depression predictions. |
| Approach: | They propose a dashboard that integrates multimodal behavioral data with multi-model signal outputs of recorded clinical interviews. |
| Outcome: | Dash-M5H is an interactive dashboard for *multi-modal, multi-model mental health assessment that integrates transcript text, audio, and facial behavior with a clinically grounded VLM prediction pipeline. |
Rescorla-Wagner Steering of LLMs for Undesired Behaviors over Disproportionate Inappropriate Context (2025.emnlp-main)
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Rushi Wang, Jiateng Liu, Cheng Qian, Yifan Shen, Yanzhou Pan, Zhaozhuo Xu, Ahmed Abbasi, Heng Ji, Denghui Zhang
| Challenge: | Incorporating external context can enhance the response quality of Large Language Models (LLMs). however, real-world contexts often mix relevant information with disproportionate inappropriate content. |
| Approach: | They propose a Poisoned Context Testbed to pair queries with real-world contexts . they propose 'rw-Steering' to internalize inappropriate signals . |
| Outcome: | The proposed model improves response quality by 39.8% and reverses undesirable behavior curve. |